Showing papers by "Heng-Da Cheng published in 2006"

Breast ultrasound image enhancement using fuzzy logic

Abstract: Breast cancer is still a serious disease in the world Early detection is very essential for breast cancer prevention and diagnosis Breast ultrasound (US) imaging has been proven to be a valuable adjunct to mammography in the detection and classification of breast lesions Because of the fuzzy and noisy nature of the US images and the low contrast between the breast cancer and tissue, it is difficult to provide an accurate and effective diagnosis This paper presents a novel algorithm based on fuzzy logic that uses both the global and local information and has the ability to enhance the fine details of the US images while avoiding noise amplification and overenhancement We normalize the images and then fuzzify the normalized images based on the maximum entropy principle Edge and textural information are extracted to describe the lesion features and the scattering phenomenon of US images and the contrast ratio measuring the degree of enhancement is computed and modified The defuzzification process is used to obtain the enhanced US images To demonstrate the performance of the proposed approach, the algorithm was tested on 86 breast US images Experimental results confirm that the proposed method can effectively enhance the details of the breast lesions without overenhancement or underenhancement

Natural Scene Segmentation Based on Information Fusion and Homogeneity Property

Abstract: This paper presents a novel approach to natural scene segmentation. It uses both color and texture features in cooperation to provide comprehensive knowledge about every pixel in the image. A novel scheme for the collection of training samples, based on homogeneity, is proposed. Natural scene segmentation is carried out using a two-stage hierarchical self-organizing map (HSOM). The proposed method confirms that the sample selection based on homogeneity and the selflearning ability and adaptability of the HSOM, coupled with the information fusion mechanism, can lead to good segmentation result, which is validated by experiments on a variety of natural scene images.